A study on estimating knee joint angle using motion sensors  under conditions of magnetic field variation

Saito, Ayuko; Nara, Yuto; Miyawaki, Kazuto

doi:10.1299/transjsme.19-00061

“…Then, the prediction step [Eqs. (14) and (15)] and the filtering step [Eqs. (18), (19), (20)] are calculated using the nonlinear discrete-time system represented by Eqs.…”

Section: State-space Modelmentioning

confidence: 99%

“…Using information obtained from the motion sensors, several sensor fusion algorithms have been proposed for pose estimation: as one example, a sensor fusion algorithm that can correct gyroscope drift using information obtained from the other two sensors has been used for human pose estimation during daily activities and exercise [11][12][13]. Furthermore, a sensor fusion algorithm able to correct the magnetometer output using information obtained from a gyroscope has been used for pose estimation in a variable magnetic field [14,15]. The Kalman filter [16][17][18][19][20] and the complementary filter [21][22][23][24][25] are some pose estimation methods using sensor fusion.…”

Section: Introductionmentioning

confidence: 99%

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Saito

¹

,

Kizawa

²

,

Kobayashi

³

et al. 2020

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This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants were running in place for about 100 s. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

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“…One observation equation proposed for use in such methods uses the yaw angle calculated from the magnetometer output and takes advantage of the fact that the accelerometer detects only the acceleration due to gravity when it is at rest [18]. Because the magnetic field cannot be measured correctly using a magnetometer in a variable magnetic field, several sensor fusion methods that can correct the magnetometer output under a variable magnetic field have been proposed [19,20]. Moreover, sensor fusion approaches that consider accelerations other than the acceleration due to gravity have been proposed [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Knee joint angle estimation by sequential correction of gyroscope bias

Saito

¹

,

Morıchı

²

,

Kizawa

³

2022

2022 International Symposium on Micro-NanoMehatronics and Human Science (MHS)

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Compact and lightweight nine-axis motion sensors have come to be used for motion analysis in a variety of fields such as medical care, welfare, and sports. Nine-axis motion sensors include a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and can estimate joint angles using the gyroscope outputs. However, the bias of the gyroscope is often unstable depending on the measurement environment and the accuracy of the gyroscope itself, causing error to accumulate in the angle obtained by integrating the gyroscope output. Although several sensor fusions have been proposed for pose estimation, such as using an accelerometer and a magnetometer, sequentially estimating and correcting the bias of the gyroscope are desirable for more accurate pose estimation. In addition, considering accelerations other than the acceleration due to gravity is important for a sensor fusion method that utilizes the accelerometer to correct the gyroscope output. Therefore, in this study, an extended Kalman filter algorithm was developed to sequentially correct both the gyroscope bias and the centrifugal and tangential acceleration of an accelerometer. The gait measurement results indicate that the proposed method successfully suppresses drift in the estimated knee joint angle over the entire measurement time of knee angle measurement during gait. The knee joint angles estimated using the proposed method were generally consistent with results obtained from an optical 3D motion analysis system. The proposed method is expected to be useful for estimating motion in medical care and welfare applications.

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“…One observation equation proposed for use in such methods uses the yaw angle calculated from the magnetometer output and takes advantage of the fact that the accelerometer detects only the acceleration due to gravity when it is at rest [18]. Because the magnetic field cannot be measured correctly using a magnetometer in a variable magnetic field, several sensor fusion methods that can correct the magnetometer output under a variable magnetic field have been proposed [19,20]. Moreover, sensor fusion approaches that consider accelerations other than the acceleration due to gravity have been proposed [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Knee joint angle estimation by sequential correction of gyroscope bias

Saito

¹

,

Tanzawa

²

,

Kizawa

³

2021

Preprint

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0

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Compact and lightweight nine-axis motion sensors have come to be used for motion analysis in a variety of fields such as medical care, welfare, and sports. Nine-axis motion sensors include a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and can estimate joint angles using the gyroscope outputs. However, the bias of the gyroscope is often unstable depending on the measurement environment and the accuracy of the gyroscope itself, causing error to accumulate in the angle obtained by integrating the gyroscope output. Although several sensor fusions have been proposed for pose estimation, such as using an accelerometer and a magnetometer, sequentially estimating and correcting the bias of the gyroscope are desirable for more accurate pose estimation. In addition, considering accelerations other than the acceleration due to gravity is important for a sensor fusion method that utilizes the accelerometer to correct the gyroscope output. Therefore, in this study, an extended Kalman filter algorithm was developed to sequentially correct both the gyroscope bias and the centrifugal and tangential acceleration of an accelerometer. The gait measurement results indicate that the proposed method successfully suppresses drift in the estimated knee joint angle over the entire measurement time of knee angle measurement during gait. The knee joint angles estimated using the proposed method were generally consistent with results obtained from an optical 3D motion analysis system. The proposed method is expected to be useful for estimating motion in medical care and welfare applications.

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A study on estimating knee joint angle using motion sensors under conditions of magnetic field variation

Cited by 3 publications

References 7 publications

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Knee joint angle estimation by sequential correction of gyroscope bias

Knee joint angle estimation by sequential correction of gyroscope bias

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